We tend to think of language, perception and thought as representing the world, pointing towards and mapping reality. But AI’s large language models suggest that this isn’t how language works, argues cognitive scientist Elan Barenholtz. These models prove that language and imagery can produce coherent continuations without ever grounding themselves in external reality. This “autogenerative” capacity was always present in language, awaiting discovery. And, Barenholtz argues, it is now the best explanation we have of how language and perception work in humans. Meaning arises not by establishing facts about the world, but rather through language’s generative role in producing further language, imagery, and, ultimately, coordinated human action.
Imagine that archaeologists unearth clay tablets from an ancient civilization, long lost to the world. There are no bilingual texts, no known descendants of the civilization, nothing to anchor a translation of the tablets. They seem to display nothing more than rows of arbitrary squiggles. Now imagine that someone claims to have decoded the squiggles. “These patterns,” they assure us, “are self-predicting. The sequence of symbols in one part of the tablet is mathematically sufficient to derive what will appear in another part.” And, indeed, they produce an algorithm which correctly predicts the text on the right side of each tablet based on the text on the left side.
The finding that the symbols contain this predictive structure would be an extraordinary insight. But we still might ask, “what do the symbols mean?” Now replace the tablets with the digital corpus we call the internet, and the algorithm with a large language model. The civilization is ours. And the question of meaning is ours too.
